JW-HEP#140034 736..743


NEW HORIZONS

Systems Biology for Hepatologists
Jos�e M. Mato,1 M. Luz Mart�ınez-Chantar,1 and Shelly C. Lu2

Medicine is expected to benefit from combining usual cellular and molecular studies
with high-throughput methods (genomics, transcriptomics, proteomics, and metabolo-
mics). These methods, collectively known as omics, permit the determination of thou-
sands of molecules (variations within genes, RNAs, proteins, metabolites) within a
tissue, cell, or biological fluid. The use of these methods is very demanding in terms of
the design of the study, acquisition, storage, analysis, and interpretation of the data.
When carried out properly, these studies can reveal new etiological pathways, help to
identify patients at risk for disease, and predict the response to specific treatments. Here
we review these omics methods and mention several applications in hepatology research.
(HEPATOLOGY 2014;60:736-743)

T
here is a nautical chart attributed to Christo-
pher Columbus, obviously drawn before he
set sail on the voyage that would lead to the

discovery of America, that stretches from the south
of Scandinavia to the mouth of the river Congo
showing all the Mediterranean ports of Europe and
Africa in detail. The enormous space that Columbus
dedicated to the Atlantic Ocean is conspicuously
lacking in detail. In all probability, this huge blank
space served not only to mark the frontier of the
known world and therefore the potential expansion
of world knowledge, it also opened up a route for
the imagination and the adventure of sailing through
it, a route traveled by numerous sixteenth- and
seventeenth-century explorers who, although in most
cases were destined to remain anonymous, changed
the world forever.

In the same way, sequencing the human genome
opened up a new era in biomedical sciences that is
being explored by a legion of scientists. Biomedical
research has evolved from the analysis of the effects of
individual genes to a more integrated view that exam-
ines whole ensembles of genes as they interact during a
biological process. This has changed the way we look
at human disease and understand better why specific

therapies work or do not work. An example in hepato-
logy is the use of a genetic variation near the IL28B
gene that predicts the response to hepatitis C therapy.1

This way of thinking has given excessive value, how-
ever, to a way of carrying out research in biosciences
that consists of measuring everything (genes, proteins,
metabolites) in a biological system in the hope that
upon analyzing this huge amount of information, new
properties of the system will emerge that will allow an
integral understanding. This holistic approach often
forgets that in biology the interactions between mole-
cules (DNA, RNA, proteins, and metabolites) are
characterized not by exclusivity, but by the multiplicity
of possible interactions between some molecules and
others. The problem is that it is not possible to dis-
cover how an organism works based on a model that
incorporates hundreds of thousands of measurements
of its internal components simply because there is no
single solution, no predefined design, not a unique 3D
structure. From this perspective, health or disease can-
not be viewed as the result of the fulfillment of a lin-
ear program, but the result of an open process in
which a specific biological state springs from certain
genetic information interacting with other information
existing at that moment.

Abbreviations: DCA, deoxycholic acid; DMRs, differentially methylated regions; ENCODE, Encyclopedia of DNA Elements; FA, fatty acids; GC, gas chromatog-
raphy; GWAS, genome-wide association study; HCC, hepatocellular carcinoma; MAT1A, methionine adenosyltransferase 1A; MS, mass spectrometry; NAFLD,
nonalcoholic fatty liver disease; NASH, nonalcoholic steatohepatitis; PC, phosphatidylcholine; PE, phosphatidylethanolamine; PHB1, prohibtin-1; SAMe, S-adeno-
sylmethionine; SNP, single nucleotide polymorphism; TG, triglycerides; TMAO, trimethylamine N-oxide; UPLC, ultraperformance liquid chromatography.

From the 1CIC bioGUNE, Ciberehd, Parque Tecnol�ogico de Bizkaia, Bizkaia, Spain; 2Division of Gastroenterology and Liver Diseases, USC Research Center
for Liver Diseases, The Southern California Research Center for Alcoholic and Pancreatic Diseases & Cirrhosis, Keck School of Medicine USC, Los Angeles, CA,
USA.

Received October 1, 2013; accepted January 15, 2014.
Supported by NIH RO1AT1576 (to M.L.M-C., S.C.L., J.M.M.), RO1DK051719 (to S.C.L., J.M.M.), Spanish Plan Nacional I1D SAF 2011-29851

(to J.M.M.), ETORTEK-2010 Gobierno Vasco (to M.L.M.-C, J.M.M.), PI11/01588, Sanidad Gobierno Vasco 2008, Educaci�on Gobierno Vasco 2011 (to
M.L.M.-C), 2012 (J.M.M.). Ciberehd is funded by ISCiii.

736



This vision helps to understand how multiple phe-
notypes can be formed from a single genome, and
how environment and chance select, at each moment,
one from among all possible phenotypes (Fig. 1). Even
in those diseases caused by mutations in a single gene,
as in the case of phenylketonuria, although the geno-
type predicts well the biochemical phenotype (the con-
centration of phenylalanine in the blood), it does not
predict the clinical phenotype (the appearance of intel-
lectual disability).2 It is important to emphasize that
clinical reasoning is basically Bayesian. In other words,
the predictive value associated with a diagnostic test
varies when it is applied to populations with indices of
prevalence very different from those of the study con-
dition. For example, in a person diagnosed with iron
overload the presence of a mutation in the HFE gene
is a highly reliable predictor of hereditary hemochro-
matosis. However, in a population that has not been
preselected for iron overload, the presence of the same
mutation confers only a slight risk of developing clini-
cal symptoms.3 These results speak for themselves of
the importance of interpreting the results of studies of
genetic variations within an adequate medical context.

Genomics

When carried out within the adequate medical con-
text, genetic screens are powerful tools for identifying
new genes and variations within genes that are involved
in specific physiopathological processes. For example, a
single nucleotide polymorphism (SNP) that is consis-
tently associated with nonalcoholic fatty liver disease
(NAFLD) is a nonsynonymous substitution (a mutation
in which a single nucleotide change results in a codon
that codes for a different amino acid) in the PNPLA3
gene.4 To identify this gene variant, a genome-wide
association study (GWAS) of 9,299 nonsynonymous
sequence variations was carried out in a population of
2,111 individuals from three different ethnic groups, in
which hepatic fat content was measured by proton mag-
netic resonance spectroscopy.5 The substitution of an
isoleucine by a methionine at position 148 (I148M) of
PNPLA3 was found to associate strongly with the accu-
mulation of fat across the three ethnic groups studied,
with an overall P-value of 5.9E-10. Recently, this variant

was associated with NAFLD progression to nonalco-
holic steatohepatitis (NASH), alcoholic fatty liver dis-
ease, and hepatocellular carcinoma (HCC).6-8 The
experience of identifying PNPLA3 teaches us that this
hypothesis-free approach to the identification of new
genes and variations within genes involved in a patho-
logical process needs to be statistically sound and
requires a large sample size of clinically well-
characterized patients.

Through December 2013, 2,034 GWAS have been
published that have led to the identification of several
hundreds of disease-associated gene variants.9 GWAS
are approaches that aim to identify potential associated
genes, at the whole genome level, based on the statisti-
cal significance of the differential occurrence of com-
mon SNPs when comparing populations with distinct
traits such as disease and health or drug responders
and nonresponders.10 Interestingly the majority of
these SNPs are not in gene coding regions, which sug-
gests that these variants affect regulatory elements of
the genome that have key functions in the develop-
ment of complex diseases, such as those of the liver.

Fig. 1. Metabolic fluxes are the best representation of the pheno-
type of an organism. Health or disease cannot be viewed as the result
of the fulfillment of a linear program, but as the result of an open pro-
cess in which a specific biological state or phenotype springs from
certain genetic information interacting with other information existing
at that moment. This vision helps to understand how multiple pheno-
types can be formed from a single genome, and how environment and
chance select, at each moment, one from among all possible
phenotypes.

Address reprint requests to: Prof. Jos�e M. Mato, CIC bioGUNE, Parque Tecnol�ogico de Bizkaia, 48160 Derio, Bizkaia, Spain. E-mail: director@cicbiogune.es;
fax: 134-944-0611301.

Copyright VC 2014 by the American Association for the Study of Liver Diseases.
View this article online at wileyonlinelibrary.com.
DOI 10.1002/hep.27023
Potential conflict of interest: Dr. Mato consults for and owns stock in Owl. He consults for Abbott.

HEPATOLOGY, Vol. 60, No. 2, 2014 MATO ET AL. 737



This agrees with the results of the Encyclopedia of
DNA Elements (ENCODE) consortium, an ambitious
project that aims to identify and characterize all func-
tional elements in the human genome,11 whose princi-
pal conclusion is that the majority of the genome,
although not coding for proteins, is active and plays
important regulatory functions.12

Transcriptomics and Proteomics

Genomic technologies have made feasible investigating
the expression of thousands of genes at a time using large
sets of samples. This technology has often been used with
the aim to develop tests that more reliably diagnose dis-
eases and predict the response to specific treatments.
However, the clinical application of these diagnostic and
prognostic gene expression signatures has been delayed
for three main reasons. First, complex diseases, like
NASH, cirrhosis, or HCC, likely involve a large number
of different genes and biological pathways and are very
heterogeneous in terms of clinical manifestations,
genomic alterations, and gene expression patterns.13-15

Therefore, large cohorts of well-characterized patients are
necessary to obtain genomic signatures of clinical rele-
vance. Second, diagnostic and prognostic gene signatures
contain a large number of genes and the prediction algo-
rithms are complex and not easy to use routinely in clin-
ics.16 Third, the development of complex molecular tests
based on DNA, RNA, proteins, or metabolite profiles
carries a series of problems inherent to all high-
throughput techniques where large datasets are ana-
lyzed.17 Selecting the statistically significant results from a
large dataset also containing nonsignificant data is chal-
lenging, because when multiple significance tests are cal-
culated the probability that at least one reaches
significance by chance increases with the number of tests
performed.18 It is therefore critical to control this multi-
plicity problem as well as to use one or more model vali-
dation technique for assessing how the results of a
statistical analysis will generalize to an independent data-
set.18 However, in the race to apply genomics technology,
genomics works are too frequently published in which
massive quantities of data containing avoidable errors are
handled.17 Yet when used correctly, microarray technolo-
gies may be translated into score systems that can repro-
ducibly predict clinical outcomes. An example in
hepatology is the development of a simple risk score clas-
sifier based on the expression of a small number of genes
that can predict in a reproducible manner overall survival
of patients after surgical resection for HCC.19 In a recent
report, the Institute of Medicine identified best practices
for future research and development of omics-based

tests.20 These practices include the use of rigorous statisti-
cal methods, bioinformatics and data management, and
open access to the datasets and algorithms used to
develop the test. The application of these best practices
should be reinforced by all research organizations.21

One of the most important objectives of genomic
research is also to associate transcriptomic data with
the molecular pathways that underlie disease. However,
gene expression changes in complex diseases, such as
those of the liver, often reflect processes that are sec-
ondary to the pathological process. To overcome this
problem, transcriptional networks have been developed
based on the assumption that gene products that are
causative of a disease process and whose expression is
altered in a pathological condition have similar expres-
sion patterns (coexpressed genes).22 An impressive
example of this approach is the use of an integrative
genomic method, based on the analysis of transcrip-
tional networks in human brain, to identify a new
molecular pathway linking late-onset Alzheimer’s dis-
ease, aging, and APOE4.23 The same principle applies
to proteomic research, where the concentration of
hundreds to thousands of proteins is determined
simultaneously in a cell type or tissue. An application
of this method in hepatology is the demonstration that
knocking out the liver-specific prohibitin-1 gene
(Phb1) in mice results in the spontaneous development
of severe liver disease and HCC24 after identification,
using proteomics, that liver PHB1 content was
decreased in an experimental model of NASH.25

One of the best ways to learn about the function of a
gene is to generate a mouse with a deletion in that spe-
cific gene. Hence, the function of around 7,300 mouse
genes has been described using this approach.26 In gen-
eral, these knockout mice have been generated towards
genes previously studied and where a phenotype was
anticipated, as in the case of Phb1 mentioned above.
Interestingly, on many occasions no obvious phenotype is
observed, probably because only the expected traits are
investigated. As a result of this, the full biological func-
tion of many genes for which knockout mice are available
is not known. Genome-wide mouse programs aiming to
generate knockout mice with mutations in all protein
coding genes are under way.26,27 Over 900 knockout
mice, many with phenotypic data, have been made
openly available for further analysis.27 It is important to
note that even when the deletion of a known gene is asso-
ciated with a phenotype, elucidating the molecular mech-
anism by which mutation of that gene leads to a
particular phenotype is not obvious at first glance and
requires extensive experimental work to elucidate it.

738 MATO ET AL. HEPATOLOGY, August 2014



The coordinated changes in gene expression patterns
associated with the molecular pathways that underlie
disease are controlled at multiple levels. Examples
include nucleosome remodeling, noncoding RNAs,
histone variants, and modifications (e.g., by acetylation
or methylation).28 MicroRNAs and RNA-binding pro-
teins play a critical role in the posttranscriptional regu-
lation of global changes in gene expression.11,12 DNA
methylation is another key epigenetic modulator of
gene expression that is generally associated with tran-
scriptional repression.11,12 Large-scale DNA methyla-
tion mapping studies have provided important insights
into the gene regulation and the development of vari-
ous diseases, particularly in tumorigenesis.29 One of
the most important objectives of DNA-methylation
mapping research is to link DNA methylation changes
with the expression of genes pathways that underlie
disease. Here, similar to genome research, a variety of
software has been released to facilitate the identifica-
tion of differentially methylated regions (DMRs), clas-
sification of DMRs into enriched genomic regions,
and comparison of DNA methylation, and gene
expression changes. Recently, this technique has ele-
gantly been applied to identify differences in DNA
methylation that could distinguish patients with
advanced versus mild NAFLD,30 and that led to the
identification of MAT1A (methionine adenosyltransfer-
ase 1A) as one of the principal down-regulated genes
in NASH.31 These findings agree with earlier work
demonstrating that MAT1A expression and MAT activ-
ity are markedly reduced in human liver cirrhosis,32,33

and that MAT1A expression is silenced in human
HCC.34 Furthermore, deletion of Mat1a in mice
causes NASH and HCC,35,36 which support the con-
cept that from NASH to HCC MAT1A may be a
therapeutic target.37

Metabolomics

Metabolomics, the high-throughput identification
and quantification of small-sized (<1,500 Da) mole-
cules, is the last branch of omics-based technology
incorporated into biomedical research. While in other
omics fields thousands of targets are routinely analyzed
at a time, until recently few studies had identified and
quantified more than 100 metabolites at a time in a
large set of samples. Two factors have made it feasible to
determine the concentration of hundreds of metabolites
at a time using large sets of samples. First, the release of
an electronic database equivalent to GenBank or Uni-
Prot, like the Human Metabolome Database; and sec-
ond, the development of modern high-resolution

nuclear magnetic resonance (NMR) spectroscopy and of
mass spectrometry (MS) technologies, such as ultraper-
formance liquid chromatography-MS (UPLC-MS) and
gas chromatography-MS, for the identification and
quantification of thousands of metabolites at a time in
as little as a few minutes per sample.

The human serum metabolome is composed, with
today’s technology, of around 4,200 metabolites, half of
which are phospholipids and over a thousand glyceroli-
pids (triglycerides [TG], diglycerides, and monoacylgly-
cerols).38 In other words, around three-quarters of the
known human serum metabolome are lipids. Amino
acids, peptides, carbohydrates, amines, and carboxylic
acids complete the list of the serum metabolome. Thou-
sands of different lipids seem much more than the four
bases used by DNA to encode the genetic information of
an organism, much more than the 23 amino acids that
are the building blocks of proteins, much more than the
hundreds of carbohydrates and carboxylic acids that
form the central carbon metabolism. But ultimately, this
many thousands of lipids make sense, if we think, for
instance, that an average car has over 10,000 moving
parts. From the storage of energy and the establishment
of the permeability barrier for cells and cell organelles, to
the regulation of membrane-associated processes, such as
oxidative phosphorylation, intracellular trafficking, cell
growth, apoptosis, and the facilitation of membrane pro-
tein folding in a manner similar to protein molecular
chaperones, lipids play an essential biological function.

Metabolic dysfunction has been implicated in a
wide variety of human diseases, such as obesity,
NAFLD, diabetes, inborn errors of metabolism, and
cancer, just to mention a few.39 The results are consist-
ent with an important contribution of metabolic disba-
lance, that is, the rerouting of the metabolic fate of
lipids, carbohydrates, and amino acids through the
intermediary metabolism, to the initiation and/or pro-
gression of these and other diseases. Consequently,
there is an increased interest in understanding what
the metabolic differences are between normal and dis-
eased tissues that can lead to the development of more
selective and effective treatments. Cellular metabolism
consists of a multitude of enzymatic reactions, inextri-
cably interconnected, that are involved in two func-
tions: one, the conversion of thousands of molecules
into building blocks for macromolecular biosynthesis;
and two, the reactions that ensure the constant supply
of energy by way of adenosine triphosphate (ATP) and
redox equivalents (NADPH). The concentrations of
the metabolites in a cell are the result of the fluxes in
the metabolic reactions, which ultimately depend on

HEPATOLOGY, Vol. 60, No. 2, 2014 MATO ET AL. 739



the conditions of the moment such as the available
nutrients, hormonal and neural factors, the properties
of the enzymes involved, and the levels of the metabo-
lites themselves, as they exert important feedback and
feedforward regulation on the system.40 Notoriously,
the liver parenchyma shows a zonal distribution of key
metabolic enzymes and metabolism, which indicates
that there are different types of hepatocytes in the
liver. For instance, oxidative phosphorylation, glucose
output, urea synthesis, and bile acid synthesis is higher
in the periportal area, whereas glucose uptake, gluta-
mine formation, and xenobiotic metabolism are greater
in the perivenous area.41 Metabolic zonation is altered
in liver steatosis,42 but whether this reflects processes
that are secondary to the pathological process is an
open question. From this perspective, it is clear that
the metabolic fluxes represent the final outcome of cel-
lular regulation at many different levels, and hence
they are the best representation of the phenotype of an
organism (Fig. 1).

A consequence of this convoluted network of enzy-
matic reactions that integrate cellular metabolism is
that it is not possible to conclude that a certain meta-
bolic pathway is altered in a cell or tissue simply by
measuring the steady-state concentration of its metabo-
lites at a single timepoint. For example, the three
major sources of fatty acids (FA) used by the liver to

synthesize TG are the diet, de novo lipogenesis, and
the adipose tissue; and the four major fates of hepatic
FA are mitochondrial b-oxidation, biosynthesis of
other lipid classes, esterification and storage as TG
into lipid droplets, and assembly as TG into very low-
density lipoproteins and export into blood (Fig. 2).
Processes that lead to an imbalance between the intake
and biosynthesis of TG and the export and catabolism
of TG cause NAFLD. Elucidating which of all these
potential mechanisms are responsible for hepatic TG
accumulation under a specific condition requires care-
ful measurements of metabolic fluxes, using labeled
tracers, as well as the determination of the content of
dozens to hundreds of metabolites and activities of key
enzymes. Unfortunately, studies are too frequently
published in which a pathological process is associated
with changes in a certain metabolite or group of
metabolites based simply in their steady-state concen-
tration, or quantification of mRNA and/or protein of
specific enzymes. Moreover, it is important to remem-
ber that changes in the concentration of metabolites
often reflect processes that are secondary to the patho-
logical process. However, when used correctly meta-
bolic studies may lead to the identification of the rate-
limiting step responsible of a pathological process. An
example in hepatology is the identification that an
excess of hepatic S-adenosylmethionine (SAMe), which

Fig. 2. FA metabolism in liver. The three major sources of FA used by the liver to synthesize triglyceride (TG) are the diet, de novo lipogenesis,
and the adipose tissue; and the four major fates of hepatic FA are mitochondrial b-oxidation, biosynthesis of other lipid classes (such as phos-
pholipids, cholesterol esters, and sphingolipids), esterification, and storage as TG into lipid droplets (LD), and assembly as TG into very low-
density lipoproteins (VLDL) and export into blood. Hepatic TG can be synthesized by desaturation, elongation, and esterification of FA, or by the
phosphatidylethanolamine N-methyltransferase (PEMT) pathway that converts phosphatidylethanolamine (PE) to phosphatidylcholine (PC). TG
export by way of VLDL requires incorporation of PC synthesized by the PEMT pathway.

740 MATO ET AL. HEPATOLOGY, August 2014



occurs in the setting of impaired glycine N-methyl-
transferase-mediated catabolism, reroutes phosphatidy-
lethanolamine (PE) metabolism towards the
biosynthesis of phosphatidylcholine (PC), by way of
activation of the enzyme PE methyltransferase (Fig. 2).
The excess PC thus generated is used by the hepato-
cyte to synthesize TG that accumulate into lipid drop-
lets, causing NAFLD.43

The human metabolome is an ocean full of bio-
markers. Accordingly, a central objective in metabolomics
research is the discovery of specific metabolic profiles (in
serum, urine, feces, sweet, tears, saliva, tissues) that associ-
ate with disease or the response to specific treatments.
The development of metabolomic-based diagnostic and
prognostic tests has the same problems inherent to all
high-throughput techniques, namely, the detection of
statistically significant relationships between a group of
metabolites and disease while minimizing the risk of
false-positive associations.18,44 An additional complica-
tion in metabolomics, as compared to other omics-based
methods, is the preparation and storage of the samples,
due to large differences in solubility and stability among
metabolites. When used correctly, metabolomics is a
powerful novel approach for biomarker identification.
For example, a serum lipidomic signature associated with
NAFLD progression has been identified.45 To obtain this
signature, 540 serum metabolites were determined by
UPLC-MS in a population of 467 biopsied individuals
with different body mass indexes.45,46

Microbiome

In addition to the 22,000 or so protein-coding genes of
the human genome, the collective genome of the human
gut flora is guessed to contain 100 to 200 times that num-
ber.47 This collective genome, the microbiome, provides
us with an additional and extraordinary metabolic
capacity that modulates host energy and lipid metabo-
lism,48 whose importance in health and disease we are
beginning to understand. Thus, gut microbiota alterations
have been associated with the susceptibility of developing
certain diseases such as obesity, diabetes, celiac disease, car-
diovascular disease, and NASH.48 An example of this
complex relationship between the gut microbes and the
host metabolism is the discovery of a new pathway for gut
flora-mediated generation of the pro-atherosclerotic
metabolite trimethylamine N-oxide (TMAO) from die-
tary PC.49 Another example that illustrates the complex
relationship between gut microbiota and liver disease is
the demonstration that bile acid metabolism by intestinal
bacteria has a key role in obesity-associated HCC develop-
ment.50 Those authors analyzed the serum metabolites of

high-fat-diet and normal-diet-fed mice by UPLC/MS and
observed an increase in the levels of deoxycholic acid
(DCA), a secondary bile acid solely produced by hydroxy-
lation of primary bile acids by gut bacteria. DCA is
known to cause DNA damage and enhance liver cancer.
Interestingly, lowering DCA levels in obese mice treated
with the carcinogen dimethylbenz(a)anthracene decreased
HCC development.50 These results speak for themselves
of the complex relationship between the gut flora and the
host metabolism and the importance to assess medical
risks, monitor, diagnose, and treat patients according to
their specific metabolic phenotype.

In conclusion, the ultimate aim of omics-based
research in hepatology is to translate this knowledge into
useful results that improve our understanding of complex
biological processes, make reliable predictions in silico of
human liver drug toxicity, and provide clinically relevant
tests (Fig. 3). However, several problems need to be over-
come to ensure the successful translation of these

Fig. 3. Omics-based medicine. The ultimate aim of omics-based
medicine is to translate human genomics, transcriptomics, proteomics,
and metabolomics results into clinically useful products. To help
researchers achieve this goal, several freely accessible initiatives have
been established, such as the Genome Sequencing Program (GSP),
the Encyclopedia of DNA Elements (ENCODE), the Genetic Variation
Program (GVP), or the Genome-Wide Associations Studies (GWAS) of
the National Human Genome Research Institute (http://www.genome.
gov/). In transcriptomics, the Gene Expression Omnibus (GEO) pro-
vides a public repository that archives and freely distributes (http://
www.ncbi.nlm.nih.gov/geo/info/overview.html) microarrays and other
functional genomics data. In proteomics, the Human Proteome Organi-
zation (HUPO, http://www.hupo.org/initiatives/) sponsors several initia-
tives such as the Human Liver Protein Project (HLPP) or the Human
Antibody Initiative (HAI); and in metabolomics the Human Metabolome
Database (HMDB, http://www.hmdb.ca/) and other related resources
such as KEGG, LipidMaps, and MassBank, that contain freely available
information about metabolites found in the human body.

HEPATOLOGY, Vol. 60, No. 2, 2014 MATO ET AL. 741

http://www.genome.gov/
http://www.genome.gov/
http://www.ncbi.nlm.nih.gov/geo/info/overview.html
http://www.ncbi.nlm.nih.gov/geo/info/overview.html
http://www.hupo.org/initiatives/
http://www.hmdb.ca/


technologies. One is adopting protocols that yield con-
sistent results in different laboratories so that data can be
built into a single repository. Another problem is the inte-
gration of all the data generated by omics-based screens
(such as RNAs, proteins, metabolites, protein-protein
interactions, protein-lipid interactions, protein-nucleic
acid interactions, and so on). Once these two problems
are solved, the translation of omics-based results into clin-
ically useful products will be within reach.

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